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Modeling the Ga/As binary system across temperatures and compositions from first principles

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Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

Changpeng Lin, Hong Zhang, Chen Shen, Yong Zhao

Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottlen ...

NATURE PORTFOLIO2023

Twisting instabilities in elastic ribbons with inhomogeneous pre-stress: A macroscopic analog of thermodynamic phase transition

Pedro Miguel Nunes Pereira de Almeida Reis, Michael Christopher Gomez

We study elastic ribbons subject to large, tensile pre-stress confined to a central region within the cross-section. These ribbons can buckle spontaneously to form helical shapes, featuring regions of alternating chirality (phases) that are separated by so ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations

Vasiliki Tileli, Reinis Ignatans

Analysis of the temperature- and stimulus-dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature-induced phase transition in BaTiO3 is explored using the machine learning ana ...

WILEY-V C H VERLAG GMBH2022

Tensor network investigation of the hard-square model

Frédéric Mila, Samuel Louis Nyckees

Using the corner-transfer matrix renormalization group to contract the tensor network that describes its partition function, we investigate the nature of the phase transitions of the hard-square model, one of the exactly solved models of statistical physic ...

AMER PHYSICAL SOC2022

Molecular simulations allow to investigate the behaviour of materials at the atomistic level, shedding light on phenomena that cannot be directly observed in experiments. Accurate results can be obtained with ab initio methods, while simulations of large-s ...

EPFL2021

In this letter we present a finite temperature approach to a high-dimensional inference problem, the Wigner spiked model, with group-dependent signal-to-noise ratios. For two classes of convex and non-convex network architectures the error in the reconstru ...

IOP Publishing Ltd2021

Crystallography, thermodynamics and phase transitions in refractory binary alloys

Anirudh Raju Natarajan

We investigate phase stability in all binary alloys comprised of elements from groups 4 (Ti, Zr, Hf), 5 (V, Nb, Ta) and 6 (Cr, Mo, W) of the periodic table. First-principles calculations of the energy landscapes along crystallographic pathways that connect ...

Elsevier2020

In 1952, Frank (Proc R Soc Lond Ser-Math Phys Sci 215:43-46, 1952) already postulated that Icosahedral Short Range Order (ISRO) of atoms in the liquid could possibly explain the large nucleation undercoolings measured in metallic alloys by Turnbull and Fis ...

SPRINGER2020

We develop a path-integral dynamics method for water that resembles centroid molecular dynamics (CMD), except that the centroids are averages of curvilinear, rather than Cartesian, bead coordinates. The curvilinear coordinates are used explicitly only when ...

2019

Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

Michele Ceriotti, Félix Benedito Clément Musil, Michael John Willatt

Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes it possible to im ...

2018